Surface integrity of ball burnished bioresorbable magnesium alloy

Magnesium alloys are potential biodegradable and biocompatible implant materials because of their excellent biological properties. Recently, interest in these alloys as a promising alternative for temporary orthopedic implants has grown owing to their desirable biological, mechanical, and physical properties. However, the application of magnesium alloys is hindered by their rapid degradation and low corrosion resistance in physiological fluids, leading to the failure of implants. Thus, the current challenge is to enhance the corrosion resistance and control the degradation rate of magnesium under physiological conditions. The rapid degradation of magnesium alloys can be controlled by improving their surface integrity, such as surface roughness and microhardness. The present study aims to improve the surface integrity of the Mg Ze41A alloy by the ball burnishing technique. The surface roughness improved by 94.90% from 0.941 μm to 0.048 μm with a burnishing force of 50 N, burnishing speed of 1 300 r/min, burnishing feed of 130 mm/min, and three passes. Similarly, the microhardness improved by 50.62% from 75.2 HV to 113.27 HV with a burnishing force of 60 N, burnishing speed of 1 100 r/min, burnishing feed of 100 mm/min, and five passes. The variations in microhardness, which were observed up to 400 μm beneath the surface, exhibited a linear nature. These variations may be attributed to the movement of dislocations, formation of new dislocations, nanocrystal structures, metastable phases and subgrains, and lattice distortion or grain refinement. The surface features obtained from optical images demonstrated the fundamental mechanisms involved in the ball burnishing process. The concept of burnishing maps and zones will assist in the design of the ball burnishing parameters of a material with an equivalent yield strength of 140 MPa. The significant improvement in the surface integrity of the Mg Ze41A alloy by the ball burnishing technique is expected to improve its functional performance.

Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

In the present study, artificial neural network(ANN) approach was used to predict the stress–strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network(NN) to predict the stress–strain curve. In order to achieve this, three different NN architectures(feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network), three different transfer functions(purelin, Log-Sigmoid, and Tan-Sigmoid), number of hidden layers(1 and 2), number of neurons in the hidden layer(s),and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set(a,16 % a, 40 % a, and b stress–strain curves). After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress–strain curve of near b titanium alloy.

Novel design and composition optimization of self-lubricating functionally graded cemented tungsten carbide cutting tool material for dry machining

The functionally graded cemented tungsten carbide (FGCC) is a suitable material choice for cutting tool applications due to balanced hardness and fracture toughness.The presence of cobalt and CaF2 composition gradient in FGCC may enhance mechanical as well as antifriction properties.Therefore,structural design of selflubricating FGCC was proposed using Power law composition gradient model and thermal residual stresses (TRSs) as a key parameter.Wherein,S.Suresh and A.Mortensen model was adopted for estimation of TRS,and optimum composition gradient was identified at Power law exponent n =2.The designed material displayed compressive and tensile TRS at surface and core respectively;subsequently fabricated by spark plasma sintering and characterized via scanning electron microscope (SEM),indentation method.The agreement between experimental and analytical values of TRS demonstrated the effectiveness of intended design model in the composition optimization of self-lubricating FGCC.This work will be helpful in implementation of dry machining for clean and green manufacturing.

Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites

The present study aims at introducing a newly developed natural fiber called castor oil fiber,termed ricinus communis,as a possible reinforcement in tribo-composites.Unidirectional short castor oil fiber reinforced epoxy resin composites of different fiber lengths with 40%volume fraction were fabricated using hand layup technique.Dry sliding wear tests were performed on a pin-on-disc tribometer based on full factorial design of experiments(DoE)at four fiber lengths(5,10,15,and 20 mm),three normal loads(15,30,and 45 N),and three sliding distances(1,000,2,000,and 3,000 m).The effect of individual parameters on the amount of wear,interfacial temperature,and coefficient of friction was studied using analysis of variance(ANOVA).The composite with 5 mm fiber length provided the best tribological properties than 10,15,and 20 mm fiber length composites.The worn surfaces were analyzed under scanning electron microscope.Also,the tribological behavior of the composites was predicted using regression,artificial neural network(ANN)-single hidden layer,and ANN-multi hidden layer models.The confirmatory test results show the reliability of predicted models.ANN with multi hidden layers are found to predict the tribological performance accurately and then followed by ANN with single hidden layer and regression model.